(1) Field of the Invention
The present invention relates to a method for performing a test for bimodulating on a data set. More specifically, the present invention relates to a method for using a neural network to empirically discern the modality of a data set.
(2) Description of Related Art
The determination of whether or not a sampled data set exhibits a uni-modal characteristic can be critical to how the data is processed and analyzed. For example, in the area of gas turbine performance diagnostic trending, it is necessary to initialize the process by determining the (initial) performance state of the engine from a sample of engine data. A pivotal requirement to achieve this is to insure that the data sample be homogeneous, i.e., that it does not exhibit a bi-modal feature. In this example, a bi-modal characteristic might arise from a sudden trend shift in the data due to a performance change, degradation, or hardware failure or even a maintenance corrective action. Since the process involves statistical manipulations (averaging, calculating scatter, etc.), it is critical to insure that the sample remains homogeneous. Formal statistical tests for homogeneity are either non-existent or too complex for automatic real-time implementation.
A simple method for determining bi-modality in a given data set is to visually inspect the sample. The construction of a histogram aids greatly in this determination. If the sample histogram 1 is homogeneous, it will yield one mode 3 and appear as illustrated in FIG. 1. Conversely, bi-modal data will produce a histogram as illustrated in FIG. 2.
The distinguishing factor that separates the two histograms 1 of FIGS. 1 and 2 is the presence of 2 peaks (modes) 3 in the histogram 1 of FIG. 2. Human vision is capable naturally of distinguishing whether or not a data set under consideration possesses a twin peak characteristic. What is therefore needed is a method of automatically accomplishing, by use of a computer, that which human vision accomplishes naturally. Specifically, there is needed a simple empirically based methodology for the purpose of identifying bi-modal data.